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Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China

Wen WU Yiquan WANG

WU Wen, WANG Yiquan, 2021. Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China. Chinese Geographical Science, 31(6): 1045−1056 doi:  10.1007/s11769-021-1238-0
Citation: WU Wen, WANG Yiquan, 2021. Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China. Chinese Geographical Science, 31(6): 1045−1056 doi:  10.1007/s11769-021-1238-0

Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China

Funds: Under the auspices of the China National R&D Program (No. 2017YFC0505704), National Natural Science Foundation of China (No. 32101325), Fundamental Research Funds for the Central Universities of China (No. N2011005) , Student Innovation Training Program of Northeastern University of China (No. 201299)
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  • Figure  1.  Distribution of public open spaces in the center of Shenyang City

    Figure  2.  Flow chart for particle swarm algorithm (PSA) in Shenyang

    Figure  3.  Results yielded by k-mean clustering algorithm in Shenyang

    Figure  4.  Layout of optimization points in Shenyang

    Figure  5.  Transport accessibility of three different modes of transport in Shenyang

    Table  1.   Service radius-based classification of urban public open spaces in Shenyang

    ClassificationPrincipal target population of serviceSize (ha)Service radius (m)Accessibility by walking (m)
    Comprehensive parkUrban residents≥ 5040002000
    [20–50) 3000
    [10–20)2000
    Community parkCommunity residents[3–10)10001000
    [1–3) 800
    Residential estate-level parkResidents in the residential estate< 1 500 500
    下载: 导出CSV

    Table  2.   Parameters for different modes of transport in Shenyang

    Mode of transportSpeed / (km/h)Time interval / minUpper time limit / minNode resistance / s
    Walking4.353030
    Bicycle1556030
    Motor vehicleArterial road60106030
    Collector road40
    Frontage road30
    下载: 导出CSV

    Table  3.   Urban public open space accessibility rating in Shenyang

    Accessibility ratingQuantityProportion / %
    Good (≥ 38.125) 466 16.4
    Moderately good [27.380–38.125) 82 2.9
    Average [16.636–27.380) 295 10.4
    Moderately poor [5.892–16.636) 742 26.2
    Poor (< 5.892) 1241 43.9
    下载: 导出CSV

    Table  4.   Location of optimization points for urban public open space in Shenyang

    Optimization pointX-coordinate / °Y-coordinate / °Location
    1 123.4702 41.7275 Near Zhenhai Hospital, Fumin Street, Hunnan District
    2 123.4565 41.8269 Near Jixiao Alley, Dadong District
    3 123.2615 41.7593 Near Fourth Street, Tiexi District
    4 123.4622 41.8432 Near Yalu River East Street, Huanggu District
    5 123.5156 41.8379 Near East Ertai Street, Dadong District
    6 123.3942 41.7317 Near Xiudao Road, Hunnan District
    7 123.5301 41.8169 Near Gaoguantai Street, Shenhe District
    8 123.3818 41.7999 Near Xiaobei East Road, Tiexi District
    下载: 导出CSV

    Table  5.   Proportions of accessible urban public open spaces before and after optimization in Shenyang

    Motor vehicle / mina1 / %a2 / %Bicycle / minb1 / %b2 / %Walking / minc1 / %c2 / %
    < 102.332.54< 50.290.31< 50.050.05
    10–207.938.405–101.221.335–100.210.22
    20–3015.5316.4310–152.953.2110–150.490.53
    30–4025.2626.4715–307.988.4915–200.900.97
    40–6037.8639.2230–6016.5517.1920–302.212.39
    > 6011.096.94> 6071.0269.46> 3096.1495.83
    Note: See Fig. 5 for definitions of acronyms
    下载: 导出CSV
  • [1] Chen Ziyan, Qiu Bing, 2019. Studies on urban open space and public health in China. Journal of Nanjing Forestry University (Humanities and Social Sciences Edition), 19(2): 58–66. (in Chinese)
    [2] Cheng Min, Huang Weiwei, 2020. Measuring the accessibility of residential care facilities in shanghai based on Gaussian two-step floating catchment area method. Journal of Fudan University (Natural Science), 59(2): 129–136. (in Chinese)
    [3] Comber A, Brunsdon C, Green E, 2008. Using a GIS-based network analysis to determine urban greenspace accessibility for different ethnic and religious groups. Landscape and Urban Planning, 86(1): 103–114. doi:  10.1016/j.landurbplan.2008.01.002
    [4] Dai D J, 2010. Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health & Place, 16(5): 1038–1052. doi:  10.1016/j.healthplace.2010.06.012
    [5] Dai D J, 2011. Racial/ethnic and socioeconomic disparities in urban green space accessibility: where to intervene? Landscape and Urban Planning, 102(4): 234–244. doi:  10.1016/j.landurbplan.2011.05.002
    [6] Deborah O M L, Chiu M Y L, Cao K et al., 2018. Geographical accessibility of community health assist scheme general practitioners for the elderly population in Singapore: a case study on the elderly living in housing development board flats. International Journal of Environmental Research and Public Health, 15(9): 1988. doi:  10.3390/ijerph15091988
    [7] Fan P L, Xu L H, Yue W Z et al., 2017. Accessibility of public urban green space in an urban periphery: the case of Shanghai. Landscape and Urban Planning, 165: 177–192. doi:  10.1016/j.landurbplan.2016.11.007
    [8] Geng Qingge, Yang Qingshan, Jiang Xiaojun, 2019. Research progress of the urban public space in english literature in the past 20 years: mapping knowledge domains analysis based on citespace. Modern Urban Research, (9): 82–89. (in Chinese)
    [9] Hao Lijun, Xiao Zhetao, Deng Rongxin et al., 2019. The construction of greenway ecological network coupling with urban space in the central urban area of Zhengzhou. Ecological Economy, 35(10): 224–229. (in Chinese)
    [10] Higgs G, Langford M, Norman P, 2015. Accessibility to sport facilities in Wales: a GIS-based analysis of socio-economic variations in provision. Geoforum, 62: 105–120. doi:  10.1016/j.geoforum.2015.04.010
    [11] Hillsdon M, Panter J, Foster C et al., 2006. The relationship between access and quality of urban green space with population physical activity. Public Health, 120(12): 1127–1132. doi:  10.1016/j.puhe.2006.10.007
    [12] Jorgensen A, Anthopoulou A, 2007. Enjoyment and fear in urban woodlands-does age make a difference? Urban Forestry & Urban Greening, 6(4): 267–278. doi:  10.1016/j.ufug.2007.05.004
    [13] Kennedy J, Eberhart R C, 1997. A discrete binary version of the particle swarm algorithm. In: Proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics: Computational Cybernetics and Simulation. Orlando, Florida: IEEE, 4104–4109. doi: 10.1109/icsmc.1997.637339
    [14] Kessel A, Green J, Pinder R et al., 2009. Multidisciplinary research in public health: a case study of research on access to green space. Public Health, 123(1): 32–38. doi:  10.1016/j.puhe.2008.08.005
    [15] La Rosa D, 2014. Accessibility to greenspaces: GIS based indicators for sustainable planning in a dense urban context. Ecological Indicators, 42: 122–134. doi:  10.1016/j.ecolind.2013.11.011
    [16] Li Weiqiang, Gao Hei, Guo Hongzhi, 2020. Design method of ‘Upgrading’ greenway from the perspective of ‘Urban Double Repair’: taking Tonglu Fuchun Greenway as an example. Huazhong Architecture, 38(9): 68–72. (in Chinese)
    [17] Li Xin, Ma Xiaodong, Xue Xiaotong et al., 2019. Spatial supply-demand evaluation and layout optimization for urban green space: a case study of xuzhou central district. Scientia Geographica Sinica, 39(11): 1771–1779. (in Chinese)
    [18] Lu Yanchi, Fang Shiming, 2019. Study on the spatial distribution optimization of park green spaces in the main city of wuhan from the perspective of both equilibrium and efficiency. Resources and Environment in the Yangtze Basin, 28(1): 68–79. (in Chinese)
    [19] Luo W, Qi Y, 2009. An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15(4): 1100–1107. doi:  10.1016/j.healthplace.2009.06.002
    [20] McGrail M R, 2012. Spatial accessibility of primary health care utilising the two step floating catchment area method: an assessment of recent improvements. International Journal of Health Geographics, 11(1): 50. doi:  10.1186/1476-072X-11-50
    [21] Naylor K B, Tootoo J, Yakusheva O et al., 2019. Geographic variation in spatial accessibility of U.S. healthcare provider. PLoS One, 14(4): e0215016. doi:  10.1371/journal.pone.0215016
    [22] Potestio M L, Patel A B, Powell C D et al., 2009. Is there an association between spatial access to parks/green space and childhood overweight/obesity in Calgary, Canada? International Journal of Behavioral Nutrition and Physical Activity, 6: 77. doi:  10.1186/1479-5868-6-77
    [23] Richardson E, Pearce J, Mitchell R et al., 2010. The association between green space and cause-specific mortality in urban New Zealand: an ecological analysis of green space utility. BMC Public Health, 10(1): 240. doi:  10.1186/1471-2458-10-240
    [24] Shenyang Municipal People’s Government, 2017. Shenyang City Master Plan (2011–2020)
    [25] Shi Y, Eberhart R C, 1998. A modified particle swarm optimizer. In: Proceedings of 1998 IEEE International Conference on Evolutionary Computation. Anchorage, AK: IEEE, 69–73. doi: 10.1109/icec.1998.699146
    [26] Talen E, 2001. School, community, and spatial equity: an empirical investigation of access to elementary schools in West Virginia. Annals of the Association of American Geographers, 91(3): 465–486. doi:  10.1111/0004-5608.00254
    [27] Wan N, Zhan F B, Zou B et al., 2012. A relative spatial access assessment approach for analyzing potential spatial access to colorectal cancer services in Texas. Applied Geography, 32(2): 291–299. doi:  10.1016/j.apgeog.2011.05.001
    [28] Wei Fang, Li Wangming, Huangpu Jiaqun, 2016. A study on urban open space planning standards. City Planning Review, 40(7): 74–80. (in Chinese)
    [29] Wei Ye, Xiu Chunliang, Gao Rui et al., 2014. Evaluation of green space accessibility of Shenyang using Gaussian based 2-step floating catchment area method. Progress in Geography, 33(4): 479–487. (in Chinese)
    [30] Wolch J R, Byrne J, Newell J P, 2014. Urban green space, public health, and environmental justice: the challenge of making cities ‘just green enough’. Landscape and Urban Planning, 125: 234–244. doi:  10.1016/j.landurbplan.2014.01.017
    [31] Wu J Y, Chen H T, Wang H Y et al., 2020a. Will the opening community policy improve the equity of green accessibility and in what ways? - Response based on a 2-step floating catchment area method and genetic algorithm. Journal of Cleaner Production, 263: 121454. doi:  10.1016/j.jclepro.2020.121454
    [32] Wu W, Li C L, Liu M et al., 2020b. Change of impervious surface area and its impacts on urban landscape: an example of Shenyang between 2010 and 2017. Ecosystem Health and Sustainability, 6(1): 1767511. doi:  10.1080/20964129.2020.1767511
    [33] Wu W, Li L D, Li C L, 2021. Seasonal variation in the effects of urban environmental factors on land surface temperature in a winter city. Journal of Cleaner Production, 299: 126897. doi:  10.1016/j.jclepro.2021.126897
    [34] Xu Chengdong, Wang Jin, 2020. Study on accessibility optimization of urban recreation green space. Chinese Landscape Architecture, 36(4): 128–133. (in Chinese)
    [35] Xu Shan, He Zhongyu, 2020. Research on the influence of community green open space on the satisfaction of neighborhood communication. Modern Urban Research, (8): 44–52. (in Chinese)
    [36] Xue Xiaotong, Yong Xinqin, Li Xin et al., 2019. Evaluation of spatial equity of urban green space based on internet big data for central area of Xuzhou city. Ecological Science, 38(6): 140–148. (in Chinese)
    [37] Yang Jianzhao, Gao Nan, Wang Hongbao, 2020. Study on the selection behavior of urban public park for recreationists based on hotelling model. Forestry Economics, 42(3): 47–58. (in Chinese)
    [38] Ye Shen, Zheng Dehua, Chen Bing et al., 2020. Spatial accessibility of urban park green space based on service radius. Geospatial Information, 18(4): 65–69, 67. (in Chinese)
    [39] Ye T T, Zhao N Z, Yang X C et al., 2019. Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model. Science of the Total Environment, 658: 936–946. doi:  10.1016/j.scitotenv.2018.12.276
    [40] Zhai Yujia, Zhou Conghui, 2019. Comparison among urban park accessibility evaluation models based on empirical cases. Chinese Landscape Architecture, 35(1): 78–83. (in Chinese)
    [41] Zhou Yuan, Shi Tiemao, Hu Yuanman et al., 2011. Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model. Chinese Journal of Applied Ecology, 22(12): 3307–3314. (in Chinese)
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  • 收稿日期:  2021-01-08
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Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China

doi: 10.1007/s11769-021-1238-0
    基金项目:  Under the auspices of the China National R&D Program (No. 2017YFC0505704), National Natural Science Foundation of China (No. 32101325), Fundamental Research Funds for the Central Universities of China (No. N2011005) , Student Innovation Training Program of Northeastern University of China (No. 201299)
    通讯作者: WU Wen. E-Mail: wuwen@mail.neu.edu.cn

English Abstract

WU Wen, WANG Yiquan, 2021. Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China. Chinese Geographical Science, 31(6): 1045−1056 doi:  10.1007/s11769-021-1238-0
Citation: WU Wen, WANG Yiquan, 2021. Evaluation and Promotion of the Service Capacity of Urban Public Open Spaces Based on Improving Accessibility: A Case Study of Shenyang City, China. Chinese Geographical Science, 31(6): 1045−1056 doi:  10.1007/s11769-021-1238-0
    • In the past three decades, the construction and development of urban public open spaces in China has made cities greener, providing urban residents with spaces for everyday outdoor activities and alleviating the stress brought about by high-density urban development (Chen and Qiu, 2019). However, in the urbanization process, the importance of urban public spaces was easily neglected, and their area was compressed. Under this background, some urban public spaces are facing the risk of privatization, challenging the ‘public’ nature of public spaces. This is compounded by the noninvolvement of users in the traditional administration model and the unclear definition of the real service capacity of public open spaces in relevant standards, resulting in the inappropriate allocation of resources, loss of green justice (Geng et al., 2019), poor utilization of some urban public spaces, and insufficient provision of a comfortable living experience for urban residents.

      Foreign scholars introduced spatial accessibility into the public service domain and have conducted an abundance of empirical studies on urban public service facilities, such as education facilities (Talen, 2001), medical resources (McGrail, 2012; Wan et al., 2012; Deborah et al., 2018; Naylor et al., 2019), sports venues (Higgs et al., 2015), and public green spaces (La Rosa, 2014; Wolch et al., 2014). In the existing research, there are three main methods commonly used for investigating the accessibility of urban public open spaces: 1) the nearest neighbor method, in which the network or Euclidean distance to the nearest green space is used to measure the accessibility (Comber et al., 2008; Kessel et al., 2009); 2) area-weighted proportion method, in which the total area of green spaces in individual subareas is used to estimate the accessibility (Potestio et al., 2009; Richardson et al., 2010); and 3) gravity model method, in which a gravity model is used to measure the attractivity of green spaces with respect to a subarea, and the sum of the activities is used as the measure of the green space accessibility for that subarea (Hillsdon et al., 2006). The first two methods assume that residents choose the nearest green spaces; however, residents do not make choices that way in real life. The third method considers the free choices by residents but does not consider the supply-demand relationship between green spaces and populations (Wu et al., 2020a).

      To overcome the limitations of these methods, Dai (2011) introduced a Gaussian-based two-step floating catchment area (2SFCA) method into the evaluation of the green space accessibility in metropolitan Atlanta, United States, and achieved satisfactory results (Dai, 2011). The 2SFCA method, which is an improved version of the floating catchment area (FCA) method, effectively handles the scenario in which the supply-demand distance in the spatial scope of the FCA may be greater than the preset distance or in which the facilities in the spatial scope do not serve needs exclusively in the spatial scope (Luo and Qi, 2009; Dai, 2010). Therefore, the 2SFCA is more practical, as it considers not only the availability of public open spaces in the periphery of individual communities but also the needs of the populations of the neighboring communities. It is thus of greater practical significance to the urban public open space accessibility investigated in this study.

      In this study, based on the needs of residents for everyday walking activities, a 2SFCA method is used to analyze the current service capacity of urban public open spaces in Shenyang, China, correctly evaluate the accessibility for the communities, and identify areas with insufficient public open space. Then, the optimal sites for new park developments are determined. This study provides a reference for reconstructing urban public spaces, enhancing the environmental quality of public spaces, and improving residential comfort.

    • Shenyang is a core city in Northeast China and a core city of the Shenyang Economic Circle. Its urban public open spaces have both research and reference values (Wu et al., 2020b). In this study, recreational urban public open spaces (Comber et al., 2008) were selected, which are primarily urban parks, plazas, community parks, and some large residential estate-level parks and green spaces. This study took the center of Shenyang City as the study area (123°18′E–123°48′E, 41°36′N–41°57′N). The following six districts defined in the Shenyang City Master Plan (2011–2020) (Shenyang Municipal People’s Government, 2017) were selected: Huanggu, Dadong, Tiexi, Shenhe, Heping, and Hunnan. These six districts have higher population densities and are facing a more severe crisis of green equity.

      During the past decade, Shenyang showed a steady trend of development and expansion, driven by policies such as ‘Revitalizing the Old Industrial Base in Northeast China’ and ‘Building the Shenyang Economic Zone’, as indicated by the transformation of the Tiexi District and the development of the Hunnan New District. In addition, a large-scale transformation of urban interior space has been implemented, resulting in the continuous expansion of the urban impervious surface area.

    • According to the current code for the classification of urban public open spaces and the needs of this study, data of the urban public open spaces were thoroughly interpreted using Landsat-8 satellite imagery in 2018. The sets of images with a 15 m spatial resolution were derived from the United States Geological Survey (USGS, https://www.usgs.gov/) (Wu et al., 2021). The urban public open space classification was verified with investigations in the field, showing an interpretation accuracy of 85%. This is acceptable for use as a data source in our study. Considering the recreational nature of urban public open spaces, those with an area smaller than 0.1 ha or a width less than 15 m were not included. Fig. 1 shows the distribution of the public open spaces in the study area.

      Figure 1.  Distribution of public open spaces in the center of Shenyang City

    • This study also used data of urban road networks and populations of communities in Shenyang City. Road networks data were obtained from Open Street Map and were vectorized and modified according to Google Earth v.7.3.3. Population density distribution data with a 100 m resolution were generated using a random forest model based on the inversion of 2018 nighttime light data of Shenyang City by Ye et al., (2019), which were used in this study. Because parks and green spaces in the study area serve an area larger than the study area, population data covering an area larger than the study area were collected. Data of the geographical locations of communities were verified using the point-of-interest (POIs) data of the Baidu Map Open Platform. Based on the data mentioned above, in our study area, there are totally 52 subdistricts, 126 urban public open spaces (including residential estate-level public open spaces), and 2826 residential communities.

    • The service capacity of the existing urban public open spaces was analyzed using the 2SFCA method. The accessibility for the residential estates in the service scope was rated using the natural break method provided in ArcGIS 10.2 (ESRI INC, 2013), thereby identifying residential estates with poor accessibility in the service scope. Next, the residential estates with poor accessibility in the service scope of the existing urban public open spaces were counted, and the optimal number of clustering points was determined through repeated trials. The 2SFCA method-based accessibility evaluation involved two steps (Dai, 2011).

      Step 1: The spatial scope for a point of provision (an urban public open space) j was defined as a circular area with the point of provision as the center and a preset radius d0. The numbers of residents at the points of need (communities) k in the spatial scope were searched and weighted according to the distance between the points of provision and need using a Gaussian equation. The weighted numbers of residents (potential demanders) were summed, and then the ratio of the size of the point of provision to the sum number of residents Rj was calculated, as shown in Equation (1):

      $${R_j} = \frac{{{S_j}}}{{\displaystyle\sum\limits_{k \in \left\{ {{d_{kj}} \le {d_0}} \right\}} {} \left| {{D_k} \times g\left({{d_{ki}}} \right)} \right.}}$$ (1)

      where Sj is the service capacity of the j-th urban public open space and is expressed as the area of the available open space; Dk is the number of residents in the k-th community in the search area (dkjd0); dkj is the spatial distance between the j-th urban public open space and the k-th community in the search area; and g (dkj, d0) is a distance decay function, as shown in Equation (2):

      $$g\left({{d_{kj}},{d_0}} \right) = \left\{ \begin{array}{l} \frac{{{{\rm{e}}^{ - \left({\frac{1}{2}} \right) \cdot {{\left({\frac{{{d_{kj}}}}{{{d_0}}}} \right)}^2}}} - {{\rm{e}}^{ - \left({\frac{1}{2}} \right)}}}}{{1 - {{\rm{e}}^{ - \left({\frac{1}{2}} \right)}}}},{d_{kj}} \le {d_0} \\ 0\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;,{d_{kj}} > {d_0} \end{array} \right.$$ (2)

      Step 2: A spatial scope was established with a point of need i as the search center and a preset search radius. All points of provision h in the spatial scope were searched and weighted according to the spatial distance between the points of provision and need using a Gaussian equation. The weighted ratios of the size of the point of provision to the size of the population Rj were summed to obtain the accessibility for the i-th point of need AiF, as shown in Equation (3):

      $$A_i^F = \sum\limits_{k \in \left\{ {{d_{ih}} \le {d_0}} \right\}} {} g\left({{d_{ih}}} \right) \cdot {R_j}$$ (3)

      where dih is the spatial distance between the i-th community and the h-th urban public open space in the i-th search area and Rj is the supply-demand ratio for the h-th urban public open space in the i-th search area (dkjd0). According to the calculation process of the equation, the value of AiF can be understood as the weighting-based area of urban public open space per capita, which is the accessibility: a larger value of AiF indicates a greater urban public open space accessibility for the i-th community.

      An appropriate service radius is an important basis for planning and layout (Zhai and Zhou, 2019). This study classified urban public open spaces according to their service radius (Table 1) by referencing the standards for the classification of urban public open spaces provided in the Standard for Classification of Urban Green Space (CJJ/T85-2017) and the Code for Urban Residential District Planning and Design (GB 50180-1993).

      Table 1.  Service radius-based classification of urban public open spaces in Shenyang

      ClassificationPrincipal target population of serviceSize (ha)Service radius (m)Accessibility by walking (m)
      Comprehensive parkUrban residents≥ 5040002000
      [20–50) 3000
      [10–20)2000
      Community parkCommunity residents[3–10)10001000
      [1–3) 800
      Residential estate-level parkResidents in the residential estate< 1 500 500

      Considering the longest distance of resident walking travel and referencing the research results of Fan et al. (2017) on the mode of resident travel, residents were assumed to travel to community-level recreational spaces with a service radius of 300 m exclusively by walking and travel to regional-level recreational spaces with a service radius of 2000 m by walking (50% chance) or public transport (50%) (Fan et al., 2017). The search radius for Step 1 was set to 2 km, which was estimated based on an upper time limit of 30 min and a walking speed of 4.3 km/h.

      Based on the ‘15-min living circle’ plan, the travel distance is usually 800 to 1000 m. The search radius for Step 2 was set to 1 km, which was estimated based on an upper time limit of 15 min and a walking speed of 4.3 km/h. If the effect of the area of open spaces on their service capacity is not considered, the service capacity of small-area open spaces may very possibly be overestimated, biasing the results of Wei et al. (2016). Therefore, a distance decay function (at service radii of 500, 1000, and 2000 m) was used to account for the effect of the open space area (Ye et al., 2020). The effects of both the area and the decay distance of urban public open spaces were considered in this study, making the results more reasonable.

    • The positions of the optimal clustering points, i.e., the locations of additional urban public open spaces planned for the future, was determined using the PSA in MATLAB R2018b (MathWorks, 2018). The PSA simulates the predation behavior of bird flocks. Its fundamental core is using the information sharing among the individuals of a flock to enable the movement of the entire flock to evolve from disordered to ordered in the problem-solving space, thereby obtaining the optimal solution of a problem (Kennedy and Eberhart, 1997). In this study, the process of populations choosing urban public open spaces was simulated by conceiving residential estates with poor accessibility as a flock of birds and urban public open spaces as food. In this simulation (Fig. 2), individual residential estates were referred to as particles. Each particle had an optimal fitness value that was determined by a function.

      Figure 2.  Flow chart for particle swarm algorithm (PSA) in Shenyang

      In the initial stage, the PSA generates a population of random particles (i.e., random solutions), and the optimal solution is obtained through iteration. At each step of the iteration, the particles update themselves by tracking two extrema: 1) the historical optimal solution found by the individual particles, which is referred to as the individual extremum pbest, and 2) the historical optimal solution found by all particles in the entire domain, which is referred to as the global extremum gbest. The PSA uses Equation (3) to compute the positions of the optimization points (Shi and Eberhart, 1998):

      $${v_i} = \omega \times {v_i} + {c_1} \times rand\left({} \right) \times \left({{p_{{\rm{best}}{_i}}} - {x_i}} \right) + {c_2} \times rand\left({} \right) \times \left({{g_{{\rm{best}}{_i}}} - {x_i}} \right)$$ (3)

      where i = 1, 2, 3,..., N; N is the total number of particles in the swarm; vi is the velocity of the particles; rand () is a random number in the range of (0,1); xi is the current position of the particles; c1 and c2 are learning factors, and c1 = c2 = 2; ω is an inertia factor that determines the capacity for global optimization; and vmax is the maximum value of vi (larger than 0), where vi = vmax if vi is larger than vmax.

    • The ANP is widely used in accessibility evaluation (Xue et al., 2019) and was used to verify the optimization results in this study. Recreational public open spaces were defined as source points, urban road networks as chains, road junctions as nodes, and time spent on roads as resistance measurements. Table 2 shows the behavior configuration.

      Table 2.  Parameters for different modes of transport in Shenyang

      Mode of transportSpeed / (km/h)Time interval / minUpper time limit / minNode resistance / s
      Walking4.353030
      Bicycle1556030
      Motor vehicleArterial road60106030
      Collector road40
      Frontage road30
    • The results of the accessibility rating (Table 3) show that the proportions of urban public open spaces rated Good, Moderately Good, Average, Moderately Poor, and Poor were 16.4%, 2.9%, 10.4%, 26.2%, and 43.9%, respectively. According to the principle of calculation of the 2SFCA method, the urban public open space accessibility is, in effect, equal to the distance weighting-based area of urban public open space per capita. According to the Shenyang City Master Plan (2011–2020), the plan is to increase the area of parks and green spaces per capita to 12 m2/person in 2020. However, the results showed that the urban public open space accessibility index for 48.6% (1374 of 2826) of the residential estates was lower than the planned level.

      Table 3.  Urban public open space accessibility rating in Shenyang

      Accessibility ratingQuantityProportion / %
      Good (≥ 38.125) 466 16.4
      Moderately good [27.380–38.125) 82 2.9
      Average [16.636–27.380) 295 10.4
      Moderately poor [5.892–16.636) 742 26.2
      Poor (< 5.892) 1241 43.9

      The public open space accessibility in the center of Shenyang City (covering six districts) exhibited an uneven spatial distribution and was relatively low. More specifically, high-accessibility residential estates were mainly distributed at the boundary between the Huanggu and Tiexi districts, along the Hunhe River, in the core area of the Hunnan New Town Green Park, and near the center of the Dadong District, forming three spatial clusters located in the western, southern, and central parts of the study area, respectively. The western and central clusters were large in size, whereas the southern cluster was small, exhibiting an overall characteristic of high accessibility in core areas and low accessibility away from core areas. In particular, in the western cluster, there was the Tiexi Zhonggong Forest Park, which has a relatively large area. In the central cluster, there was the Wulihe Park and the Olympic Ecological Park, and in the southern cluster, there was the Hunnan Central Park. Therefore, the large green parks significantly impacted the urban public open space accessibility, and the accessibility tended to be higher for residential estates closer to the large parks and green spaces. The polarization characteristic of the green space accessibility above is consistent with research results on other metropolitan areas such as Shanghai (Wei et al., 2014; Cheng and Huang, 2020).

      The low-accessibility residential estates were mainly distributed in an east-west area spanning from the eastern Tiexi District along the Huanggu District’s southern border with the Heping District and an east-west area centering around Baita Street, forming two parallel spatial belts with different lengths. In the northern belt, there were many parks and green spaces; however, they were generally small. In addition, the large population densities in the old town areas resulted in an insufficient per-capita share and markedly low accessibility. The major cause underlying the spatial differentiation of the park accessibility was the mismatch between the populations and green spaces.

    • In the service capacity of the existing urban public open spaces, the accessibility was moderately poor or poor for 48.6% of the residential estates. Theoretically, the accessibility problem for these residential estates can be solved by adding a maximum of 82 urban public open spaces. The optimal number of additions was calculated using the k-mean clustering algorithm. The results showed that the appropriate number of additions fell in the range of 7 to 10.

      Using the PSA method, optimization was performed with respect to the residential estates with an accessibility lower than 16.636 in the study area. The final results of repeated verifications in light of the actual particularities showed that the spatial layout of the additions was most reasonable when the number of optimization points was set to 8 (Fig. 3, Table 4).

      Figure 3.  Results yielded by k-mean clustering algorithm in Shenyang

      Table 4.  Location of optimization points for urban public open space in Shenyang

      Optimization pointX-coordinate / °Y-coordinate / °Location
      1 123.4702 41.7275 Near Zhenhai Hospital, Fumin Street, Hunnan District
      2 123.4565 41.8269 Near Jixiao Alley, Dadong District
      3 123.2615 41.7593 Near Fourth Street, Tiexi District
      4 123.4622 41.8432 Near Yalu River East Street, Huanggu District
      5 123.5156 41.8379 Near East Ertai Street, Dadong District
      6 123.3942 41.7317 Near Xiudao Road, Hunnan District
      7 123.5301 41.8169 Near Gaoguantai Street, Shenhe District
      8 123.3818 41.7999 Near Xiaobei East Road, Tiexi District

      The clustering points for the 1983 poor-accessibility residential estates (742 Moderately Poor and 1241 Poor) were optimized through an iterative search (Table 3). Such points are the historical optimal solutions of the accessibility for the surrounding residential estates.Fig. 4 shows the layout of the optimization points—6 and 2 for the northern and southern clusters of low-accessibility residential estates, respectively, bounded by the Hunhe River. More specifically, two of the optimization points are in the Dadong District, two in the Tiexi District, and one in each of the remaining four districts.

      Figure 4.  Layout of optimization points in Shenyang

      A field survey showed that within 300 m of optimization points 1, 3, 5, and 7, there were areas of undeveloped land that could be developed into new urban public open spaces. Optimization points 2 and 8 were in residential estates, with no land available for developing new urban public open spaces. However, Shenyang University was located within 300 m of point 2. Thus, the pressure from the lack of parks and green spaces in the vicinity of this point can be alleviated through open-campus development. The pressure from the lack of parks and green spaces in the vicinity of point 8 can be alleviated by developing new residential estate-level green parks. Optimization point 4 was located near a railroad. There was no large green space in the vicinity, but there were many small strip- or dot-shaped green parks. Hence, an option for the future is to improve the connectivity between these small green spaces and the surrounding residential estates.

      The additional urban public open spaces yielded by the optimization are mainly distributed in old town areas such as the Tiexi, Huanggu, and Dadong Districts, which also indicates an overall insufficient provision of urban public open spaces in the current stage of urban construction and development, particularly in old town areas. The distribution of open spaces is characterized by a large number but small area in old town areas and a small number but large area in new town areas.

    • The accuracy of the optimization results yielded by the algorithm was verified by analyzing how the service capacity of public open spaces varied with the mode of transport using the ANP. The modes of transport included motor vehicle, bicycle, and walking. For all modes of transport, the time spent for passing through a crosswalk, underpass, or overpass was set to 30 s, i.e., the node resistance was set to 30 s. The results (Fig. 5) show that the urban public open space service ranges obtained for the three different modes of transport exhibit layer structures. Walking and bicycle travel have relatively invariant speeds and are not significantly affected by the road category; therefore, the corresponding service ranges exhibit relatively regular patterns. Motor vehicles are significantly affected by the road category, conditions, and intersections and have different speeds on different road sections; therefore, the corresponding service range exhibits an irregular pattern. Overall, the motor vehicle mode of transport has the largest service range, followed by those of bicycle and walking.

      Figure 5.  Transport accessibility of three different modes of transport in Shenyang

      The longest travel distances within the upper time limit above for the three different modes of transport were calculated. The results (Table 5) show that an area of 1165.72 km2 (88.91% of the study area) can be accessed within 60 min by motor vehicle before the optimization, and the optimization increases the area accessible within 60 min by motor vehicle by 4.67%. Bicycles can access an area of 380.03 km2 (28.98% of the study area) in 60 min before the optimization, and the optimization increases the accessible area by 5.38%. Walking can access an area of 50.66 km2 (3.86% of the study area) in 30 min before the optimization, and the optimization increases the accessible area by 8.03%. Overall, the optimization significantly improves the open space accessibility in the central Tiexi District, northeastern Shenhe District, and eastern Huanggu District and the area spanning from Baita Street, Hunnan New Area, to the Hunhe River.

      Table 5.  Proportions of accessible urban public open spaces before and after optimization in Shenyang

      Motor vehicle / mina1 / %a2 / %Bicycle / minb1 / %b2 / %Walking / minc1 / %c2 / %
      < 102.332.54< 50.290.31< 50.050.05
      10–207.938.405–101.221.335–100.210.22
      20–3015.5316.4310–152.953.2110–150.490.53
      30–4025.2626.4715–307.988.4915–200.900.97
      40–6037.8639.2230–6016.5517.1920–302.212.39
      > 6011.096.94> 6071.0269.46> 3096.1495.83
      Note: See Fig. 5 for definitions of acronyms

      The magnitudes of the change in the accessible area for the three different modes of transport were compared. The accessibility for 5 min of walking does not change, whereas that for motor vehicle travel increases by 0.21%. This is because walking has a low speed, and the accessible area in a shorter period of time is even smaller. Therefore, compared with developing more urban public open spaces at locations far from residential areas, improving the transport accessibility and public transport in the periphery of residential estates is more conducive to improving the urban public open space accessibility.

    • The modeling method used in this study considered the amount of available green spaces and the needs of populations. This consideration is critical. However, in the model, the size of the green spaces was used as the only indicator of their attractivity, whereas the actual situation was more complex. For example, the inertia of resident activities may affect the selection of green spaces. For cold-climate cities such as Shenyang, seasonal factors also have a major effect. For example, Wulihe Park is a relatively new riverside park with a variety of facilities and a good environment, but the number of visitors decreases sharply as the weather cools down. To address these factors, the model must be improved in the future (Jorgensen and Anthopoulou, 2007). For example, the carrying capacity of green spaces can be characterized using a comprehensive system of indicators rather than the green space area alone, or the number of residential estates with insufficient green space provision can be maximally reduced by converting low-efficiency industrial lands of appropriate sizes and locations into parks (Li et al., 2019). All these measures are subject to further quantitative investigation.

    • This study applied the 2SFCA method to analyze the service capacity of urban public open spaces and adopted the PSA to optimize the site selection for urban public open spaces with low accessibility. The PSA computes by simulating the predatory behavior of birds with a focus on both ‘food’ (urban public open spaces) and ‘paths’ (different results corresponding to different modes of transport). Because people tend to place more value on lower transport costs when selecting sites from accessible urban public open spaces for recreational activities (Yang et al., 2020), space and transport should be focused on in the layout optimization for urban public open spaces.

      This study shows that large parks and green spaces significantly impact the overall accessibility in a region, and the planning of urban public open spaces should ensure an appropriate layout of large parks and green spaces to prevent overconcentration and resource imbalance. For residential areas with many small urban public open spaces on the periphery, the service capacity of these urban public open spaces tends to be high if supported by a relatively convenient road transport system. This is because for some spaces, a convenient walking system compensates for the insufficient area of parks and green spaces and enables more people to access them by road, thereby reducing service blind spots and improving the service capacity of the urban public open spaces (Xu and Wang, 2020).

      For residential estates with an accessibility index smaller than 16.636 (accessibility rated as moderately poor or poor), first, new urban public open spaces, mainly the size of community parks, should be configured in clusters of residential estates with poor accessibility according to scientific calculations. Second, for those residential estates with accessibility smaller than 5.892 (accessibility rated as poor) after the layout optimization, we recommend improving the walking/bicycling system or urban public transport system in their 400-m periphery to attract more urban residents by rationally reducing the transport cost. This approach simultaneously enhances the service capacity of urban public open spaces near these residential estates. Finally, new strip- or dot-shaped green spaces or small parks should be constructed in the periphery of these residential estates following the principle of ‘developing green spaces wherever there is room’, thereby optimizing the green space layout and ensuring justice in the urban open space accessibility. Wu et al. (2020a) suggested that an open community policy is a practical measure for improving green justice.

      For urban public open spaces, the attractiveness of urban public open spaces that are large but far from residential areas should be improved by improving the road transport system and connectivity and enhancing the green space quality and entertainment, thereby gradually guiding residents to travel farther for recreational activities in urban public open spaces. The service capacity of urban public spaces that cover a small land area but have a high visitor traffic can be improved by improving their infrastructure or green space quality without changing their area.

    • This study investigated the service capacity and optimized the layout of urban public open spaces based on a comprehensive consideration of the supply of and residential demand for urban public open spaces using the 2SFCA method. The investigation into the accessibility of urban public open spaces from the perspective of residential estates focused on the demand-supply relationship between residents and urban public open spaces based on the population distribution and the road transport system. For residential estates with available park and green space areas less than 12 m2/person, the layout of urban public open spaces and the allocation of the green space sizes were optimized by referring to the Shenyang City Master Plan (2011–2020). This approach ensured fair green space service in the vicinity of each residential estate to all residents and satisfied residents’ basic demand for parks and green spaces, which is conducive to improving the happiness index of residents in the study area (Lu and Fang, 2019).

      In recent years, the urban greenway—a new green space structure—has been promoting the blending and coupling of ‘city’ and ‘green’. The urban greenway plays a vital role in achieving ‘Urban Double Repair’, namely, the restoration of the natural environment that has been damaged by urbanization and the continuous improvement of urban public services (Li et al., 2020). Urban greenway systems are typically constructed near available roads, rivers, and public open spaces to enable connectivity between ecological nodes and to realize the networked connection of the corridors.

      The site selection for urban public open spaces is a tradeoff process among multiple factors, including population density, environmental quality, and land use. Zhou et al. (2011) compared the simulation results for the optimal locations of urban parks obtained using a multiobjective location allocation model. They found that the urban air pollution level and heat island intensity affected the simulation results and that rational planning of the locations of urban parks better improved the quality of the urban ecological environment. In contrast, the small number, small size, and poor balance of ecological nodes in the overall urban public open spaces tend to be inconducive to the construction of urban greenway networks and the improvement of the urban ecological environment (Zhou et al., 2011). In a study by Hao et al. (2019), a green ecological network planning scheme model that constructed a green network by utilizing the current conditions and potential landscape, and by exploiting potential ecological patches, resulted in a lower overall degree of landscape fragmentation and considerably better networking and ecological efficacy than a scheme model that constructed a green network based on large-size high-functionality parks and green spaces. The former was associated with a higher landscape connectivity index (0.719 vs. 0.359) and higher landscape evenness index (2.627 vs. 1.136) than the latter. In this study, the construction of small urban public open spaces using the PSA fully utilized the available stock land to expand the urban green spaces. These newly built parks and green spaces are mostly situated in unfrequented regions of existing sites, thus enriching the unfrequented regions in urban public open spaces and promoting the perfection of the urban greenway network.

      Because urban public open spaces have multiple functions, their optimization not only contributes to urban construction but also improves residents’ quality of life. Xu and He (2020) investigated the impact of community green open space on the self-assessed satisfaction of neighborhood communication using an ordinal logistic regression model. They found that communication activities in green open spaces significantly improved the satisfaction with neighborhood communication (60% of residents were satisfied with neighborhood communication in green open spaces) and that community green open spaces contributed more to the satisfaction with neighborhood communication than estate- and city-level green spaces (Xu and He, 2020). Their findings agree with those of our study that community-level urban public open spaces are the preferential choice. The construction of attractive urban public open spaces with high green coverage and a suitable size can stimulate residents’ willingness to communicate and substantially increase their satisfaction with neighborhood communication. The findings of this study have high application value in the national strategy setting of Healthy China.

    • The existing research mainly focuses on the evaluation of the urban public open space accessibility, paying scant attention to the implementation strategies necessary for improving the accessibility, and thus has little relevance to the urban planning practice. This study finds that in the service capacity of the existing public open spaces in Shenyang, the accessibility for 48.6% of the residential estates is moderately poor or poor and is lower than the planned level. Starting from the living needs of residents and based on the measurement of the accessibility, a PSA was used to optimize the site selection for new green space developments. The results show that the layout is optimized by adding eight new green spaces and that the optimization is significantly effective in improving the green justice. The optimization increases the green space area accessible by motor vehicles (60 min), bicycles (60 min), and walking (30 min) by 4.67%, 5.38%, and 8.03%, respectively. The optimized solution can be used to prioritize green space developments and has practical significance at the policy level. In the current stage of urban construction and development, the city has an overall insufficient provision of public open spaces, particularly in old town areas. Determining how resources are allocated scientifically and appropriately is an arduous long-term project, and this study provides guidelines for urban planning and development. The research methods additionally provide references for the appropriate planning and layout of other public service spaces and facilities.

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